


'''simple docstring'''


import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread
from numpy import array, uinta
from PIL import Image

from digital_image_processing import change_contrast as cc
from digital_image_processing import convert_to_negative as cn
from digital_image_processing import sepia as sp
from digital_image_processing.dithering import burkes as bs
from digital_image_processing.edge_detection import canny
from digital_image_processing.filters import convolve as conv
from digital_image_processing.filters import gaussian_filter as gg
from digital_image_processing.filters import local_binary_pattern as lbp
from digital_image_processing.filters import median_filter as med
from digital_image_processing.filters import sobel_filter as sob
from digital_image_processing.resize import resize as rs

UpperCAmelCase_						:  Optional[int]				  =						imread(r'digital_image_processing/image_data/lena_small.jpg')
UpperCAmelCase_						:  List[Any]				  =						cvtColor(img, COLOR_BGR2GRAY)







def      snake_case_  (    ):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE					:			str							= cn.convert_to_negative(SCREAMING_SNAKE_CASE__	)
				# assert negative_img array for at least one True
				assert negative_img.any()







def      snake_case_  (    ):







				"""simple docstring"""





				with Image.open("""digital_image_processing/image_data/lena_small.jpg"""	) as img:
								# Work around assertion for response
								assert str(cc.change_contrast(SCREAMING_SNAKE_CASE__				, 110	)	).startswith(
								    """<PIL.Image.Image image mode=RGB size=100x100 at"""	)







def      snake_case_  (    ):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE					:			Dict							= canny.gen_gaussian_kernel(9				, sigma=1.4	)
				# Assert ambiguous array
				assert resp.all()







def      snake_case_  (    ):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE					:			Dict							= imread("""digital_image_processing/image_data/lena_small.jpg"""				, 0	)
				# assert ambiguous array for all == True
				assert canny_img.all()
				_SCREAMING_SNAKE_CASE					:			Tuple							= canny.canny(SCREAMING_SNAKE_CASE__	)
				# assert canny array for at least one True
				assert canny_array.any()







def      snake_case_  (    ):







				"""simple docstring"""





				assert gg.gaussian_filter(SCREAMING_SNAKE_CASE__				, 5				, sigma=0.9	).all()







def      snake_case_  (    ):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE					:			str							= array([[0.2_5, 0.5, 0.2_5], [0.5, -3, 0.5], [0.2_5, 0.5, 0.2_5]]	)
				_SCREAMING_SNAKE_CASE					:			Dict							= conv.img_convolve(SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	).astype(SCREAMING_SNAKE_CASE__	)
				assert res.any()







def      snake_case_  (    ):







				"""simple docstring"""





				assert med.median_filter(SCREAMING_SNAKE_CASE__				, 3	).any()







def      snake_case_  (    ):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE						,			_SCREAMING_SNAKE_CASE					:			Any							= sob.sobel_filter(SCREAMING_SNAKE_CASE__	)
				assert grad.any() and theta.any()







def      snake_case_  (    ):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE					:			Any							= sp.make_sepia(SCREAMING_SNAKE_CASE__				, 20	)
				assert sepia.all()







def      snake_case_  (    SCREAMING_SNAKE_CASE__ = "digital_image_processing/image_data/lena_small.jpg"	):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE					:			Optional[int]							= bs.Burkes(imread(SCREAMING_SNAKE_CASE__				, 1	)				, 120	)
				burkes.process()
				assert burkes.output_img.any()







def      snake_case_  (    SCREAMING_SNAKE_CASE__ = "digital_image_processing/image_data/lena_small.jpg"				, ):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE					:			int							= rs.NearestNeighbour(imread(SCREAMING_SNAKE_CASE__				, 1	)				, 400				, 200	)
				nn.process()
				assert nn.output.any()







def      snake_case_  (    ):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE					:			Optional[Any]							= """digital_image_processing/image_data/lena.jpg"""

				# Reading the image and converting it to grayscale.
				_SCREAMING_SNAKE_CASE					:			List[Any]							= imread(SCREAMING_SNAKE_CASE__				, 0	)

				# Test for get_neighbors_pixel function() return not None
				_SCREAMING_SNAKE_CASE					:			List[Any]							= 0
				_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= 0
				_SCREAMING_SNAKE_CASE					:			Optional[Any]							= image[x_coordinate][y_coordinate]

				_SCREAMING_SNAKE_CASE					:			Any							= lbp.get_neighbors_pixel(
				    SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	)

				assert neighbors_pixels is not None

				# Test for local_binary_pattern function()
				# Create a numpy array as the same height and width of read image
				_SCREAMING_SNAKE_CASE					:			Any							= np.zeros((image.shape[0], image.shape[1])	)

				# Iterating through the image and calculating the local binary pattern value
				# for each pixel.
				for i in range(0				, image.shape[0]	):
								for j in range(0				, image.shape[1]	):
												_SCREAMING_SNAKE_CASE					:			Tuple							= lbp.local_binary_value(SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	)

				assert lbp_image.any()








'''simple docstring'''


import math







def      snake_case_  (    SCREAMING_SNAKE_CASE__	):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE					:			Tuple							= math.loga(math.sqrt(4 * positive_integer + 1	) / 2 + 1 / 2	)

				return exponent == int(SCREAMING_SNAKE_CASE__	)







def      snake_case_  (    SCREAMING_SNAKE_CASE__ = 1 / 1_2345	):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE					:			str							= 0
				_SCREAMING_SNAKE_CASE					:			str							= 0

				_SCREAMING_SNAKE_CASE					:			int							= 3
				while True:
								_SCREAMING_SNAKE_CASE					:			Optional[int]							= (integer**2 - 1) / 4
								# if candidate is an integer, then there is a partition for k
								if partition_candidate == int(SCREAMING_SNAKE_CASE__	):
												_SCREAMING_SNAKE_CASE					:			int							= int(SCREAMING_SNAKE_CASE__	)
												total_partitions += 1
												if check_partition_perfect(SCREAMING_SNAKE_CASE__	):
																perfect_partitions += 1
								if perfect_partitions > 0:
												if perfect_partitions / total_partitions < max_proportion:
																return int(SCREAMING_SNAKE_CASE__	)
								integer += 1


if __name__ == "__main__":
				print(F"{solution() = }")




